{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare task1 dataset\n",
    "\n",
    "imgsize(512,512), pixelvalue(0~1), saved in .npy\n",
    "1. define functions of preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from loadFun import loadmat, kdata2img, multicoilkdata2img\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "NAME_DICT ={\"MultiCoil\" :{\"AccFactor04\":\"kspace_sub04\",\n",
    "                            \"AccFactor08\":\"kspace_sub08\",\n",
    "                            \"AccFactor10\":\"kspace_sub10\",\n",
    "                            \"FullSample\":\"kspace_full\"}, \n",
    "            \"SingleCoil\":{\"AccFactor04\":\"kspace_single_sub04\",\n",
    "                            \"AccFactor08\":\"kspace_single_sub08\",\n",
    "                            \"AccFactor10\":\"kspace_single_sub10\",\n",
    "                            \"FullSample\":\"kspace_single_full\"}}\n",
    "\n",
    "def paddingZero_np(np_data: np.array, target_shape: tuple):\n",
    "    \"\"\"\n",
    "    for img data stored in np.array, fill zeros surrounding it to match the target_shape\n",
    "    np_data: input\n",
    "    \"\"\"\n",
    "    shape = np_data.shape\n",
    "    H, W = shape[-2], shape[-1]\n",
    "    # print(shape)\n",
    "    padding_H = target_shape[0] - H\n",
    "    padding_W = target_shape[1] - W\n",
    "    # print(padding_H, padding_W)\n",
    "    if len(shape) == 4:\n",
    "        \n",
    "        padding_size = ((0, 0), (0, 0), (padding_H // 2, padding_H - padding_H // 2), (padding_W // 2, padding_W - padding_W // 2))\n",
    "        # print(padding_size)\n",
    "    else:\n",
    "        \n",
    "        padding_size = ((0, 0), (0, 0), (0, 0), (padding_H // 2, padding_H - padding_H // 2), (padding_W // 2, padding_W - padding_W // 2))\n",
    "        # print(padding_size)\n",
    "    \n",
    "    padded_np_data = np.pad(np_data, padding_size, mode='constant')\n",
    "\n",
    "    return padded_np_data\n",
    "\n",
    "def prepare_task1_dataset(base_dir, save_dir):\n",
    "    base_dir = base_dir\n",
    "    save_dir = save_dir\n",
    "    modalityName = 'Cine'\n",
    "    coilInfoSet = {'MultiCoil','SingleCoil'}\n",
    "    acc_factorSet = {'AccFactor04','AccFactor08','AccFactor10','FullSample'}\n",
    "\n",
    "    for coilInfo in coilInfoSet:\n",
    "        for acc_factor in acc_factorSet:\n",
    "            dir = os.path.join(base_dir,coilInfo,modalityName,'TrainingSet',acc_factor)\n",
    "            # save_dir = os.path.join(save_dir,coilInfo,modalityName,'TrainingSet',acc_factor)\n",
    "            print(dir)\n",
    "            if os.path.isdir(dir):\n",
    "                print('YES')\n",
    "            else:\n",
    "                print('Not exist')\n",
    "            \n",
    "            for item in os.listdir(dir):\n",
    "                patient_dir = os.path.join(dir,item)\n",
    "                lax_path = os.path.join(patient_dir, \"cine_lax.mat\")\n",
    "                sax_path = os.path.join(patient_dir, \"cine_sax.mat\")\n",
    "                \n",
    "                #lax\n",
    "                if os.path.exists(lax_path):\n",
    "                    lax = loadmat(lax_path)\n",
    "                    lax_data = lax[NAME_DICT[coilInfo][acc_factor]]\n",
    "                    print(lax_data.shape)\n",
    "                    lax_data = paddingZero_np(lax_data, (512, 512))\n",
    "                    print(lax_data.shape)\n",
    "                    if coilInfo == 'SingleCoil':\n",
    "                        print('lax Single')\n",
    "                        lax_imgs = kdata2img(lax_data)\n",
    "                    else:\n",
    "                        print('lax Multi')\n",
    "                        lax_imgs = multicoilkdata2img(lax_data)\n",
    "                    # print(lax_imgs.shape) # Here we have the same\n",
    "                    frame_num, slice_num, H, W = lax_imgs.shape\n",
    "                    for i in range(frame_num):\n",
    "                        for j in range(slice_num):\n",
    "                            img = lax_imgs[i, j, :, :]\n",
    "                            img = img / np.max(img)\n",
    "                            save_path = os.path.join(save_dir, acc_factor, \"{}_{}_{}_{}_{}_{}_{}.npy\".format(item, coilInfo, 'lax', i, j, H, W))\n",
    "                            if not os.path.exists(os.path.join(save_dir, acc_factor)):\n",
    "                                os.makedirs(os.path.join(save_dir, acc_factor))\n",
    "                            np.save(save_path, img)\n",
    "\n",
    "                #sax    \n",
    "                if os.path.exists(sax_path):\n",
    "                    sax = loadmat(sax_path)\n",
    "                    sax_data = sax[NAME_DICT[coilInfo][acc_factor]]\n",
    "                    print(sax_data.shape)\n",
    "                    sax_data = paddingZero_np(sax_data, (512, 512))\n",
    "                    print(sax_data.shape)\n",
    "                    if coilInfo == 'SingleCoil':\n",
    "                        print('sax Single')\n",
    "                        sax_imgs = kdata2img(sax_data)\n",
    "                    else:\n",
    "                        print('sax Multi')\n",
    "                        sax_imgs = multicoilkdata2img(sax_data)\n",
    "                    # print(sax_imgs.shape)\n",
    "                    frame_num, slice_num, H, W = sax_imgs.shape\n",
    "                    for i in range(frame_num):\n",
    "                        for j in range(slice_num):\n",
    "                            img = sax_imgs[i, j, :, :]\n",
    "                            img = img / np.max(img)\n",
    "                            save_path = os.path.join(save_dir, acc_factor, \"{}_{}_{}_{}_{}_{}_{}.npy\".format(item, coilInfo, 'sax', i, j, H, W))\n",
    "                            if not os.path.exists(os.path.join(save_dir, acc_factor)):\n",
    "                                os.makedirs(os.path.join(save_dir, acc_factor))\n",
    "                            np.save(save_path, img)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set \"original_dataset_path\" and \"save_prepared_dataset_path\" to your path\n",
    "\n",
    "2. execute preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/txiang/CMRxRecon/CMRxRecon/MICCAIChallenge2023/ChallengeData/MultiCoil/Cine/TrainingSet/AccFactor08\n",
      "YES\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 12, 10, 246, 512)\n",
      "(12, 12, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 204, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 9, 10, 204, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 204, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 12, 10, 204, 512)\n",
      "(12, 12, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 204, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 132, 448)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 204, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 132, 448)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 204, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 12, 10, 246, 512)\n",
      "(12, 12, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 162, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 162, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 168, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 13, 10, 204, 512)\n",
      "(12, 13, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 162, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 168, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 10, 10, 204, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 2, 10, 204, 448)\n",
      "(12, 2, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 132, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 9, 10, 246, 512)\n",
      "(12, 9, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 10, 10, 246, 512)\n",
      "(12, 10, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 204, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n",
      "(12, 3, 10, 204, 448)\n",
      "(12, 3, 10, 512, 512)\n",
      "lax Multi\n",
      "(12, 11, 10, 246, 512)\n",
      "(12, 11, 10, 512, 512)\n",
      "sax Multi\n"
     ]
    }
   ],
   "source": [
    "original_dataset_path = \"/home/txiang/CMRxRecon/CMRxRecon/MICCAIChallenge2023/ChallengeData\"\n",
    "save_prepared_dataset_path = \"/home/txiang/CMRxRecon/CMRxRecon/MICCAIChallenge2023/Task1\"\n",
    "prepare_task1_dataset(original_dataset_path, save_prepared_dataset_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. generate Training_pair.txt：every line in format of [input_path, GT_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "def generate_training_pairs(base_path):\n",
    "    acc_factorSet = {'AccFactor04','AccFactor08','AccFactor10'}\n",
    "    for acc_factor in acc_factorSet:\n",
    "        imgs_dir = os.path.join(base_path, acc_factor)\n",
    "        imgs_list = os.listdir(imgs_dir)\n",
    "        file = \"{}_rMax_512_training_pair.txt\".format(acc_factor)\n",
    "        file_obj = open(file, \"a\")\n",
    "        for img_name in imgs_list:\n",
    "            img_path = os.path.join(imgs_dir, img_name)\n",
    "            GT_path = os.path.join(base_path, 'FullSample', img_name)\n",
    "            if os.path.exists(img_path) and os.path.exists(GT_path):\n",
    "                file_obj.writelines([img_path, \" \", GT_path, \"\\n\"])\n",
    "\n",
    "generate_training_pairs(save_prepared_dataset_path)# 用preprocessed dataset路径"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. define custom Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import numpy as np\n",
    "\n",
    "class CMRxReconTask1Dataset(Dataset):\n",
    "    \n",
    "    def __init__(self, file_path, transform=None):\n",
    "        \"\"\"\n",
    "        file_path: train_pair.txt\n",
    "        transform: pytorch transform\n",
    "        \"\"\"\n",
    "        self.name_dict = {\"MultiCoil\":{\"AccFactor04\":\"kspace_sub04\",\n",
    "                                       \"AccFactor08\":\"kspace_sub08\",\n",
    "                                       \"AccFactor10\":\"kspace_sub10\",\n",
    "                                       \"FullSample\":\"kspace_full\"}, \n",
    "                          \"SingleCoil\":{\"AccFactor04\":\"kspace_single_sub04\",\n",
    "                                       \"AccFactor08\":\"kspace_single_sub08\",\n",
    "                                       \"AccFactor10\":\"kspace_single_sub10\",\n",
    "                                       \"FullSample\":\"kspace_single_full\"}}\n",
    "        self.file_path = file_path\n",
    "        \n",
    "        file_obj = open(self.file_path, \"r\")\n",
    "        self.train_pairs = file_obj.readlines()\n",
    "        file_obj.close()\n",
    "        \n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.train_pairs)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        path, GT_path = self.train_pairs[index].replace(\"\\n\",\"\").split(\" \")\n",
    "        print(path, GT_path)\n",
    "        if path.endswith('.npy'):\n",
    "            item = np.load(path)\n",
    "            GT_item = np.load(GT_path)\n",
    "            output = {\"input\": item, \"GT\": GT_item}\n",
    "            if self.transform:\n",
    "                data = np.stack((item, GT_item), axis=-1)\n",
    "                transformed_data = self.transform(data)\n",
    "                output = {\"input\": transformed_data[0,:,:].unsqueeze(0), \"GT\": transformed_data[1,:,:].unsqueeze(0)}\n",
    "            return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try Task1_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35808\n",
      "/public/home/yuewj/work/CMRxRecon/ChallengeData_img512_rMax/AccFactor04/P043_MultiCoil_lax_11_2_512_512.npy /public/home/yuewj/work/CMRxRecon/ChallengeData_img512_rMax/FullSample/P043_MultiCoil_lax_11_2_512_512.npy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc57607d6c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task1_dataset = CMRxReconTask1Dataset(file_path=\"AccFactor04_rMax_512_training_pair.txt\")\n",
    "print(len(task1_dataset))\n",
    "\n",
    "data47 = task1_dataset[47]\n",
    "inp = data47[\"input\"]\n",
    "GT = data47[\"GT\"]\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.imshow(GT-inp,cmap='gray')\n",
    "plt.imshow(GT, cmap='gray')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
